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Distributed Central Pattern Generator Model for Robotics Application Based on Phase Sensitivity Analysis

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Biologically Inspired Approaches to Advanced Information Technology (BioADIT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3141))

Abstract

A method is presented to predict phase relationships between coupled phase oscillators. As an illustration of how the method can be applied, a distributed Central Pattern Generator (CPG) model based on amplitude controlled phase oscillators is presented. Representative results of numerical integration of the CPG model are presented to illustrate its excellent properties in terms of transition speeds, robustness and independence on initial conditions. A particularly interesting feature of the CPG is the possibility to switch between different stable gaits by varying a single parameter. These characteristics make the CPG model an interesting solution for the decentralized control of multi-legged robots. The approach is discussed in the more general framework of coupled nonlinear systems, and design tools for nonlinear distributed control schemes applicable to Information Technology and Robotics.

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References

  1. Collins, J.J., Richmond, S.A.: Hard-wired central pattern generators for quadrupedal locomotion. Biological Cybernetics 71(5), 375–385 (1994)

    Article  MATH  Google Scholar 

  2. Feigenbaum, M.J.: Universal behavior in nonlinear systems. Los Alamos Science 1(1), 4–27 (1980)

    MathSciNet  Google Scholar 

  3. Fukuoka, Y., Kimura, H., Cohen, A.H.: Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts. The International Journal of Robotics Research 3-4, 187–202 (2003)

    Article  Google Scholar 

  4. Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Reviews of Modern Physics 70(1), 223–287 (1998)

    Article  Google Scholar 

  5. Grillner, S.: Control of locomotion in bipeds, tetrapods and fish. In: Brooks, V.B. (ed.) Handbook of Physiology, The Nervous System, 2, Motor Control, pp. 1179–1236. American Physiology Society, Bethesda (1981)

    Google Scholar 

  6. Grillner, S.: Neurobiological bases of rhythmic motor acts in vertebrates. Science 228(4696), 143–149 (1985)

    Article  Google Scholar 

  7. Haken, H.: Advanced Synergetics. Instability Hierarchies of Self-Organization Systems and Devices, 2nd edn. Springer Series in Synergetics, vol. 20. Springer, Heidelberg (1987)

    Google Scholar 

  8. Kelso, J.A.S., Scholz, J.P., Schöner, G.: Nonequilibrium phase transitions in coordinated biological motion: critical uctuations. Physics Letters A 118(6), 279–284 (1986)

    Article  Google Scholar 

  9. Kelso, J.A.S., Scholz, J.P., Schöner, G.: Dynamics governs switching among patterns of coordination in biological movement. Physics Letters A 134(1), 8–12 (1988)

    Article  Google Scholar 

  10. Landau, L.D., Lifshitz, E.M.: Statistical Physics, volume 5 of Course of theoretical physics, vol. 5. Pergamon Press, London (1959)

    Google Scholar 

  11. The MathWorks Inc. Matlab web site, http://www.mathworks.com

  12. Nekorkin, V.I., Velarde, M.G. (eds.): Synergetic Phenomena in Active Latices. Springer Series in Synergetics, vol. 79. Springer, Heidelberg (2002)

    Google Scholar 

  13. Noble, D.: Modeling the heart - from genes to cells to the whole organ. Science 295, 1678–1682 (2002)

    Article  Google Scholar 

  14. Peinke, J., Parisi, J., Rössler, O.E., Stoop, R.: Encounter with Chaos. Self-Organized Hierarchical Complexity in Semiconductor Experiments. Springer, Heidelberg (1992)

    Google Scholar 

  15. Pikovsky, A., Rosenblum, R., Kurths, J.: Synchronization, A universal concept in nonlinear sciences. Cambridge Nonlinear Science Series, vol. 12. Cambridge University Press, Cambridge (2001)

    Book  MATH  Google Scholar 

  16. Schöner, G., Jiang, W.Y., Kelso, J.A.S.: A synergetic theory of quadrupedal gaits and gait transitions. Journal of theoretical Biology 142, 359–391 (1990)

    Article  Google Scholar 

  17. Schreiber, I., Marek, M.: Dynamics of Oscillatory Chemical Systems. In: Modeling the Dynamics of Biological Systems. Springer Series in Synergetics, vol. 65, Springer, Heidelberg (1995)

    Google Scholar 

  18. Shik, M.L., Severin, F.V., Orlovsky, G.N.: Control of walking by means of electrical stimulation of the mid-brain. Biophysics 11, 756–765 (1966)

    Google Scholar 

  19. Taga, G.: A model of the neuro-musculo-skeletal system for human locomotion. I. Emergence of basic gait. Biological Cybernetics 73(2), 97–111 (1995)

    Article  MATH  Google Scholar 

  20. Tsuchiya, K., Aoi, S., Tsujita, K.: Locomotion control of a multi-legged locomotion robot using oscillators. In: 2002 IEEE Intl. Conf. SMC, vol. 4 (2002)

    Google Scholar 

  21. Yuasa, H., Ito, M.: Coordination of many oscillators and generation of locomotory patterns. Biological Cybernetics 63, 177–184 (1990)

    Article  MATH  Google Scholar 

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Buchli, J., Ijspeert, A.J. (2004). Distributed Central Pattern Generator Model for Robotics Application Based on Phase Sensitivity Analysis. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds) Biologically Inspired Approaches to Advanced Information Technology. BioADIT 2004. Lecture Notes in Computer Science, vol 3141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27835-1_25

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  • DOI: https://doi.org/10.1007/978-3-540-27835-1_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23339-8

  • Online ISBN: 978-3-540-27835-1

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